{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:38:44Z","timestamp":1784821124813,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["61825602"],"award-info":[{"award-number":["61825602"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583379","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"737-746","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":101,"title":["GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1624-2149","authenticated-orcid":false,"given":"Zhenyu","family":"Hou","sequence":"first","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8918-6734","authenticated-orcid":false,"given":"Yufei","family":"He","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5682-2810","authenticated-orcid":false,"given":"Yukuo","family":"Cen","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9226-4569","authenticated-orcid":false,"given":"Xiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6092-2002","authenticated-orcid":false,"given":"Yuxiao","family":"Dong","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3247-4166","authenticated-orcid":false,"given":"Evgeny","family":"Kharlamov","sequence":"additional","affiliation":[{"name":"Bosch Center for Artificial Intelligence, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3487-4593","authenticated-orcid":false,"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"On the bottleneck of graph neural networks and its practical implications. arXiv preprint arXiv:2006.05205","author":"Alon Uri","year":"2020","unstructured":"Uri Alon and Eran Yahav. 2020. On the bottleneck of graph neural networks and its practical implications. arXiv preprint arXiv:2006.05205 (2020)."},{"key":"e_1_3_2_1_2_1","volume-title":"Local graph partitioning using pagerank vectors","author":"Andersen Reid","unstructured":"Reid Andersen, Fan Chung, and Kevin Lang. 2006. Local graph partitioning using pagerank vectors. In FOCS. IEEE, 475\u2013486."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Aleksandar Bojchevski Johannes Klicpera Bryan Perozzi Amol Kapoor Martin Blais Benedek R\u00f3zemberczki Michal Lukasik and Stephan G\u00fcnnemann. 2020. Scaling graph neural networks with approximate pagerank. In KDD. 2464\u20132473.","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_3_2_1_4_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared\u00a0D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell 2020. Language models are few-shot learners. In NeurIPS Vol.\u00a033."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Mathilde Caron Hugo Touvron Ishan Misra Herv\u00e9 J\u00e9gou Julien Mairal Piotr Bojanowski and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In ICCV. 9650\u20139660.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"e_1_3_2_1_6_1","unstructured":"Jie Chen Tengfei Ma and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. In ICLR."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_1_8_1","unstructured":"Eli Chien Wei-Cheng Chang Cho-Jui Hsieh Hsiang-Fu Yu Jiong Zhang Olgica Milenkovic and Inderjit\u00a0S Dhillon. 2022. Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction. In ICLR."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Ganqu Cui Jie Zhou Cheng Yang and Zhiyuan Liu. 2020. Adaptive graph encoder for attributed graph embedding. In KDD. 976\u2013985.","DOI":"10.1145\/3394486.3403140"},{"key":"e_1_3_2_1_10_1","unstructured":"Wenzheng Feng Jie Zhang Yuxiao Dong Yu Han Huanbo Luan Qian Xu Qiang Yang Evgeny Kharlamov and Jie Tang. 2020. Graph random neural networks for semi-supervised learning on graphs. In NeurIPS."},{"key":"e_1_3_2_1_11_1","volume-title":"Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198","author":"Frasca Fabrizio","year":"2020","unstructured":"Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti. 2020. Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198 (2020)."},{"key":"e_1_3_2_1_12_1","unstructured":"Jean-Bastien Grill Florian Strub Florent Altch\u00e9 Corentin Tallec Pierre\u00a0H Richemond Elena Buchatskaya Carl Doersch Bernardo\u00a0Avila Pires Zhaohan\u00a0Daniel Guo Mohammad\u00a0Gheshlaghi Azar 2020. Bootstrap your own latent: A new approach to self-supervised learning. In NeurIPS."},{"key":"e_1_3_2_1_13_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS."},{"key":"e_1_3_2_1_14_1","unstructured":"Kaveh Hassani and Amir\u00a0Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In ICML."},{"key":"e_1_3_2_1_15_1","unstructured":"Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Doll\u00e1r and Ross Girshick. 2022. Masked autoencoders are scalable vision learners. In CVPR."},{"key":"e_1_3_2_1_16_1","unstructured":"Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR."},{"key":"e_1_3_2_1_17_1","unstructured":"Geoffrey\u00a0E Hinton and Richard Zemel. 1993. Autoencoders Minimum Description Length and Helmholtz Free Energy. In NeurIPS J.\u00a0Cowan G.\u00a0Tesauro and J.\u00a0Alspector (Eds.). Vol.\u00a06. Morgan-Kaufmann."},{"key":"e_1_3_2_1_18_1","unstructured":"Zhenyu Hou Xiao Liu Yukuo Cen Yuxiao Dong Hongxia Yang Chunjie Wang and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In KDD."},{"key":"e_1_3_2_1_19_1","volume-title":"Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430","author":"Hu Weihua","year":"2021","unstructured":"Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021. Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430 (2021)."},{"key":"e_1_3_2_1_20_1","unstructured":"Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. In NeurIPS."},{"key":"e_1_3_2_1_21_1","unstructured":"Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2019. Strategies for pre-training graph neural networks. In ICLR."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403237"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.91.012821"},{"key":"e_1_3_2_1_24_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf N","year":"2016","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_1_25_1","volume-title":"Kipf and Max Welling","author":"N.","year":"2017","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1080\/15427951.2009.10129177"},{"key":"e_1_3_2_1_27_1","volume-title":"Deepgcns: Can gcns go as deep as cnns?. In ICCV. 9267\u20139276.","author":"Li Guohao","year":"2019","unstructured":"Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019. Deepgcns: Can gcns go as deep as cnns?. In ICCV. 9267\u20139276."},{"key":"e_1_3_2_1_28_1","volume-title":"Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971","author":"Lillicrap P","year":"2015","unstructured":"Timothy\u00a0P Lillicrap, Jonathan\u00a0J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Xiao Liu Haoyun Hong Xinghao Wang Zeyi Chen Evgeny Kharlamov Yuxiao Dong and Jie Tang. 2022. Selfkg: self-supervised entity alignment in knowledge graphs. In WWW. 860\u2013870.","DOI":"10.1145\/3485447.3511945"},{"key":"e_1_3_2_1_30_1","volume-title":"Self-supervised learning: Generative or contrastive. TKDE","author":"Liu Xiao","year":"2021","unstructured":"Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. 2021. Self-supervised learning: Generative or contrastive. TKDE (2021)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Xiao Liu Shiyu Zhao Kai Su Yukuo Cen Jiezhong Qiu Mengdi Zhang Wei Wu Yuxiao Dong and Jie Tang. 2022. Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries. In KDD. 1120\u20131130.","DOI":"10.1145\/3534678.3539472"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Yao Ma Xiaorui Liu Tong Zhao Yozen Liu Jiliang Tang and Neil Shah. 2021. A unified view on graph neural networks as graph signal denoising. In CIKM.","DOI":"10.1145\/3459637.3482225"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Shirui Pan Ruiqi Hu Guodong Long Jing Jiang Lina Yao and Chengqi Zhang. 2018. Adversarially regularized graph autoencoder for graph embedding. In IJCAI.","DOI":"10.24963\/ijcai.2018\/362"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Jiwoong Park Minsik Lee Hyung\u00a0Jin Chang Kyuewang Lee and Jin\u00a0Young Choi. 2019. Symmetric graph convolutional autoencoder for unsupervised graph representation learning. In ICCV. 6519\u20136528.","DOI":"10.1109\/ICCV.2019.00662"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_1_36_1","series-title":"SIAM Journal on computing 42, 1","volume-title":"A local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning","author":"Spielman A","year":"2013","unstructured":"Daniel\u00a0A Spielman and Shang-Hua Teng. 2013. A local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning. SIAM Journal on computing 42, 1 (2013), 1\u201326."},{"key":"e_1_3_2_1_37_1","volume-title":"Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR\u201920.","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2020. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR\u201920."},{"key":"e_1_3_2_1_38_1","volume-title":"Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction. arXiv preprint arXiv:2202.09025","author":"Tang Mingyue","year":"2022","unstructured":"Mingyue Tang, Carl Yang, and Pan Li. 2022. Graph Auto-Encoder via Neighborhood Wasserstein Reconstruction. arXiv preprint arXiv:2202.09025 (2022)."},{"key":"e_1_3_2_1_39_1","unstructured":"Shantanu Thakoor Corentin Tallec Mohammad\u00a0Gheshlaghi Azar R\u00e9mi Munos Petar Veli\u010dkovi\u0107 and Michal Valko. 2022. Large-Scale Representation Learning on Graphs via Bootstrapping. In ICLR."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"crossref","unstructured":"Puja Trivedi Ekdeep\u00a0Singh Lubana Yujun Yan Yaoqing Yang and Danai Koutra. 2022. Augmentations in graph contrastive learning: Current methodological flaws & towards better practices. In WWW. 1538\u20131549.","DOI":"10.1145\/3485447.3512200"},{"key":"e_1_3_2_1_41_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_1_42_1","unstructured":"Petar Veli\u010dkovi\u0107 William Fedus William\u00a0L Hamilton Pietro Li\u00f2 Yoshua Bengio and R\u00a0Devon Hjelm. 2018. Deep Graph Infomax. In ICLR."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132967"},{"key":"e_1_3_2_1_44_1","unstructured":"Haonan Wang Jieyu Zhang Qi Zhu and Wei Huang. 2022. Augmentation-Free Graph Contrastive Learning. In AAAI."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Chen Wei Haoqi Fan Saining Xie Chao-Yuan Wu Alan Yuille and Christoph Feichtenhofer. 2022. Masked feature prediction for self-supervised visual pre-training. In CVPR. 14668\u201314678.","DOI":"10.1109\/CVPR52688.2022.01426"},{"key":"e_1_3_2_1_46_1","unstructured":"Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. PMLR."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Jun Xia Lirong Wu Jintao Chen Bozhen Hu and Stan\u00a0Z Li. 2022. SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation. In WWW. 1070\u20131079.","DOI":"10.1145\/3485447.3512156"},{"key":"e_1_3_2_1_48_1","volume-title":"Infogcl: Information-aware graph contrastive learning. NeurIPS 34","author":"Xu Dongkuan","year":"2021","unstructured":"Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, and Xiang Zhang. 2021. Infogcl: Information-aware graph contrastive learning. NeurIPS 34 (2021)."},{"key":"e_1_3_2_1_49_1","unstructured":"Zhilin Yang William Cohen and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML. PMLR 40\u201348."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Hao Yin Austin\u00a0R Benson Jure Leskovec and David\u00a0F Gleich. 2017. Local higher-order graph clustering. In KDD. 555\u2013564.","DOI":"10.1145\/3097983.3098069"},{"key":"e_1_3_2_1_51_1","unstructured":"Yuning You Tianlong Chen Yongduo Sui Ting Chen Zhangyang Wang and Yang Shen. 2020. Graph contrastive learning with augmentations. In NeurIPS."},{"key":"e_1_3_2_1_52_1","unstructured":"Hanqing Zeng Muhan Zhang Yinglong Xia Ajitesh Srivastava Andrey Malevich Rajgopal Kannan Viktor Prasanna Long Jin and Ren Chen. 2021. Decoupling the depth and scope of graph neural networks. In NeurIPS."},{"key":"e_1_3_2_1_53_1","volume-title":"Graphsaint: Graph sampling based inductive learning method. In ICLR.","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020. Graphsaint: Graph sampling based inductive learning method. In ICLR."},{"key":"e_1_3_2_1_54_1","unstructured":"Hengrui Zhang Qitian Wu Junchi Yan David Wipf and Philip\u00a0S Yu. 2021. From canonical correlation analysis to self-supervised graph neural networks. In NeurIPS."},{"key":"e_1_3_2_1_55_1","unstructured":"Yizhen Zheng Shirui Pan Vincent\u00a0Cs Lee Yu Zheng and Philip\u00a0S Yu. 2022. Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination. In NeurIPS."},{"key":"e_1_3_2_1_56_1","unstructured":"Jinghao Zhou Chen Wei Huiyu Wang Wei Shen Cihang Xie Alan Yuille and Tao Kong. 2022. ibot: Image bert pre-training with online tokenizer. In ICLR."},{"key":"e_1_3_2_1_57_1","volume-title":"Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"crossref","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In WWW. 2069\u20132080.","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_3_2_1_59_1","unstructured":"Zeyuan\u00a0Allen Zhu Silvio Lattanzi and Vahab Mirrokni. 2013. A local algorithm for finding well-connected clusters. In ICML. PMLR 396\u2013404."},{"key":"e_1_3_2_1_60_1","unstructured":"Difan Zou Ziniu Hu Yewen Wang Song Jiang Yizhou Sun and Quanquan Gu. 2019. Layer-dependent importance sampling for training deep and large graph convolutional networks. In NeurIPS."}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583379","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583379","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:23Z","timestamp":1750178243000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583379"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":60,"alternative-id":["10.1145\/3543507.3583379","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583379","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}